Global patterns of vascular plant alpha diversity

Global patterns of regional (gamma) plant diversity are relatively well known, but whether these patterns hold for local communities, and the dependence on spatial grain, remain controversial. Using data on 170,272 georeferenced local plant assemblages, we created global maps of alpha diversity (local species richness) for vascular plants at three different spatial grains, for forests and non-forests. We show that alpha diversity is consistently high across grains in some regions (for example, Andean-Amazonian foothills), but regional ‘scaling anomalies’ (deviations from the positive correlation) exist elsewhere, particularly in Eurasian temperate forests with disproportionally higher fine-grained richness and many African tropical forests with disproportionally higher coarse-grained richness. The influence of different climatic, topographic and biogeographical variables on alpha diversity also varies across grains. Our multi-grain maps return a nuanced understanding of vascular plant biodiversity patterns that complements classic maps of biodiversity hotspots and will improve predictions of global change effects on biodiversity.

The sample size of each of the 99 iterations of our analysis was 17,972 plots. This is the result of a stratified resampling,where for stratum (i.e.,m the combination of realm, biome, broad formation (two classes: forest and non-forest), and plot size (4 bins)), we randomly sampled (without replacement) up to 100 vegetation plots. If a stratum had fewer than 100 vegetation plots, we retained all of them.
Our database is a collection of 116 datasets from all over the World, each being collected according to a proper sampling scheme.
Each vegetation plot in each of the 116 datasets was surveyed in the field by trained botanists, who recorded the complete list of vascular plants occurring in the vegetation plot. Specifics on the data collection of each dataset composing the sPlot database are contained in the references contained in the Supplementary Table 1.
Each vegetation plot was surveyed only once. Sampling dates vary are comprised between 1888 and 2015. The older relevés were digitized from published resources. There is no rationale for the starti and stop dates. Rather, we tried to gather as many published data as possible. The spatial scale is global We excluded all vegetation plots taken in wetlands, as well as those that could not be confidently assigned neither to forest nor to non-forest vegetation The code for data selection,exclusion and resampling is available at: https://github.com/fmsabatini/GlobalLocal_PlantRichness.
We applied a stratified resampling strategy that we repeated 99 times. We defined each stratum as a unique combination of realm, biome, broad formation (two classes: forest and non-forest), and plot size as a factor variable with four levels (small: 150, medium: 150-600, large: 600-1200, very large: >1200 m2) Being a correlative analysis, no blinding was needed when performing the statisticla analyses.
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